Referring the synthetic data generation protocol in [1]}, we generate 100 time series sequences of length 200 from a multi-output Gaussian process with a multiple output kernel [2]}, implemented by a python package named GPy [3]}. Sequences are correlated through a random sampled positive definite coregionalization matrix, and we add random non-stationary Gaussian noise on the 100-dimensional sequence of length 200. The location of anomalies is simulated by a binary mask with Bernoulli distribution of probability {0.5%, 1%, 2%, 5%, 10%} for different fractions of anomalies. We insert Poisson noise where the binary mask is equal to one as anomalies, and the intensity \(\lambda _t\)  of each anomaly is proportional to the amplitude of inserted place \(x_t\) . The plus and minus sign is random sampled from Bernoulli distribution with \(p=0.5\) .
